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It also offers profound discoveries: pathogen biology informs all biology, because pathogens are excellent model organisms.\n\nTools like rapid genome sequencing, immunological profiling and AI will enable the speedy development of new treatments that are tailored to emerging diseases. Furthermore, improved surveillance tools have the potential to swiftly monitor and contain outbreaks, and potentially even prevent them. Achieving these goals requires interdisciplinary research and depends on publicly available systematic datasets.\n\nDespite the progress, much of the potential of this research is not being fully harnessed. Low- and middle-income countries often have limited capacity to detect, monitor and contain outbreaks.[1](/citation/2025-03-3-6-1/) This is partly the result of a lack of capacity in scientific fields like genomics and structural biology[2](/citation/2025-03-3-6-2/) — and means many countries are effectively flying blind when it comes to pandemic preparedness. In other countries, governments are reducing their support for pathogen biology and related fields.[3](/citation/2025-03-3-6-3/) Researchers are anxious for policy-makers to see the value of continued research and development, which will be crucial in averting the worst impacts of the next pandemic.[4](/citation/2025-03-3-6-4/)\n\n\n**KEY TAKEAWAYS**\n\nPathogens — organisms that infect us and cause disease — remain a grave threat to health and well-being. One level of research into this threat aims to unpick the mechanisms by which pathogens enter our cells, effectively **Decoding infectivity**. Improved understanding of these mechanisms will enable novel treatments and preventative strategies. A related effort focuses on **Zoonotics and evolution across species**: how pathogens that infect animals make the leap to infecting humans, and how we might prevent this. Similar research enables improved **Epidemiology and prediction**: outbreaks can now be tracked using multiple data sources. Finally, many teams are focused on **Emerging opportunities for intervention**, which step beyond existing tools like antibiotics to explore phage therapies, mRNA vaccines and more."},"intro":{"text":"Despite many lessons having been learned from the COVID-10 pandemic, it is inevitable that humanity will face another pandemic. This prospect is decidedly uncomfortable, but can also be a provocation to create innovations in research and healthcare. These will be of significant value: even in the absence of a global outbreak, pathogens pose a significant ongoing threat to human health and wellbeing."},"anticipatoryImpact":{"text":"Three fundamental questions guide GESDA’s mission and drive its work: Who are we, as humans? How can we all live together? How can we ensure the well-being of humankind and the sustainable future of our planet? We asked researchers from the field to anticipate what impact future breakthroughs could have on each of these dimensions. This wheel summarises their opinions when considering each of these questions, with a higher score indicating high anticipated impact, and vice versa.\n\n* Anticipated impact on who we are as humans\n* Anticipated impact on how we will all live together\n* Anticipated impact on the well-being of humankind and sustainable future of our planet"},"indicatorValues":[{"id":"65c55cf49e947c438698aaac","value":"0.572","numericValue":0.572,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68a68b6f0ac1330579fd78ed","value":"0.6185","numericValue":0.6185,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"editions":[{"id":"66ab1bb636a8f2f336a557bf","name":"2024","slug":"2024","numericValue":2024},{"id":"684951c963371e51d83bdf31","name":"2025","slug":"2025","numericValue":2025}],"anticipatoryImpactImage":{"image":{"id":"image_gesda-platform/image-asset/psp-pl-3-25-3-6_image__PSP-PL3_25_3.6_tzfgrl","url":"https://res.cloudinary.com/shapeable/image/upload/v1760070387/gesda-platform/image-asset/psp-pl-3-25-3-6_image__PSP-PL3_25_3.6_tzfgrl.webp"}},"embeds":{"citations":[{"id":"691a7a65c0043bba84a9eb47","slug":"2025-03-3-6-1","url":"https://doi.org/10.1038/s41591-024-03081-9","name":"Building genomic capacity for precision health in Africa","authors":[{"id":"691a7a65c0043bba84a9eb45","name":"A. Olono et al.","slug":"a-olono-et-al"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature Medicine","accessDate":null,"startPage":1856,"volume":30,"footnoteNumber":1,"year":null},{"id":"691a7a66c0043bba84a9eb4b","slug":"2025-03-3-6-2","url":"https://doi.org/10.1038/s41588-024-01807-6","name":"Establishing African genomics and bioinformatics programs through annual regional workshops","authors":[{"id":"691a7a66c0043bba84a9eb49","name":"A. Sharaf et al.","slug":"a-sharaf-et-al"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature Genetics","accessDate":null,"startPage":1556,"volume":56,"footnoteNumber":2,"year":null},{"id":"691a7a67c0043bba84a9eb4f","slug":"2025-03-3-6-3","url":"https://doi.org/10.1038/s41577-025-01166-1","name":"How to respond when biomedical science and global health is under existential threat","authors":[{"id":"691a7a66c0043bba84a9eb4d","name":"D. M. Altmann and A. L. Rasmussen","slug":"d-m-altmann-and-a-l-rasmussen"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature Reviews Immunology","accessDate":null,"startPage":313,"volume":25,"footnoteNumber":3,"year":null},{"id":"691a7a67c0043bba84a9eb53","slug":"2025-03-3-6-4","url":"https://doi.org/10.1128/jvi.01791-23","name":"Virology — the path forward","authors":[{"id":"691a7a67c0043bba84a9eb51","name":"A. L. Rasmussen et al.","slug":"a-l-rasmussen-et-al"}],"authorShowsEtAl":null,"edition":null,"publication":"Journal of Virology","accessDate":null,"startPage":null,"volume":98,"footnoteNumber":4,"year":null}],"imageAssets":[]},"surveyObservations":{"text":"Infectious diseases remain a major threat to human health, with climate change causing new diseases to emerge or existing diseases to affect new populations. Advances in our understanding of pathogen biology will allow the design of new interventions but this still requires significant research. These sub-topics’ high disruptive potentials also contribute to the high anticipation scores. **Zoonotics and evolution across species** remains a highly transformative area of research, with impactful breakthroughs expected in the coming 10 years."},"color":{"id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B","darkValue":"#066152","veryDarkValue":"#02201b"},"banner":{"id":"6a9246199d83c3b6c148556b","name":"\"Bacteria, everywhere\" by Julien Luneau, University of Lausanne","description":{"text":"\"Bacteria, everywhere\" by Julien Luneau, University of Lausanne"},"image":{"id":"image_gesda-platform/banner/bacteria-everywhere-by-julien-luneau-university-of-lausanne_image__22Bacteria_everywhere_22_by_Julien_Luneau_University_of_Lausanne_dgqxcs","url":"https://res.cloudinary.com/shapeable/image/upload/v1787971085/gesda-platform/banner/bacteria-everywhere-by-julien-luneau-university-of-lausanne_image__22Bacteria_everywhere_22_by_Julien_Luneau_University_of_Lausanne_dgqxcs.jpg","thumbnails":{"mainBanner":{"url":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_1440/v1787971085/gesda-platform/banner/bacteria-everywhere-by-julien-luneau-university-of-lausanne_image__22Bacteria_everywhere_22_by_Julien_Luneau_University_of_Lausanne_dgqxcs.jpg","url2x":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_2880/v1787971085/gesda-platform/banner/bacteria-everywhere-by-julien-luneau-university-of-lausanne_image__22Bacteria_everywhere_22_by_Julien_Luneau_University_of_Lausanne_dgqxcs.jpg"}}}},"chartImage":null,"citations":[{"__typename":"Platform_Citation","_schema":{"label":"Citation","pluralLabel":"Citations"},"typeLabel":"Journal","slug":"2025-03-3-6-1","url":"https://doi.org/10.1038/s41591-024-03081-9","name":"Building genomic capacity for precision health in Africa","authors":[{"id":"691a7a65c0043bba84a9eb45","name":"A. Olono et al.","slug":"a-olono-et-al"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature Medicine","accessDate":null,"startPage":1856,"volume":30,"footnoteNumber":1,"year":null},{"__typename":"Platform_Citation","_schema":{"label":"Citation","pluralLabel":"Citations"},"typeLabel":"Journal","slug":"2025-03-3-6-2","url":"https://doi.org/10.1038/s41588-024-01807-6","name":"Establishing African genomics and bioinformatics programs through annual regional workshops","authors":[{"id":"691a7a66c0043bba84a9eb49","name":"A. Sharaf et al.","slug":"a-sharaf-et-al"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature Genetics","accessDate":null,"startPage":1556,"volume":56,"footnoteNumber":2,"year":null},{"__typename":"Platform_Citation","_schema":{"label":"Citation","pluralLabel":"Citations"},"typeLabel":"Journal","slug":"2025-03-3-6-3","url":"https://doi.org/10.1038/s41577-025-01166-1","name":"How to respond when biomedical science and global health is under existential threat","authors":[{"id":"691a7a66c0043bba84a9eb4d","name":"D. M. Altmann and A. L. Rasmussen","slug":"d-m-altmann-and-a-l-rasmussen"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature Reviews Immunology","accessDate":null,"startPage":313,"volume":25,"footnoteNumber":3,"year":null},{"__typename":"Platform_Citation","_schema":{"label":"Citation","pluralLabel":"Citations"},"typeLabel":"Journal","slug":"2025-03-3-6-4","url":"https://doi.org/10.1128/jvi.01791-23","name":"Virology — the path forward","authors":[{"id":"691a7a67c0043bba84a9eb51","name":"A. L. Rasmussen et al.","slug":"a-l-rasmussen-et-al"}],"authorShowsEtAl":null,"edition":null,"publication":"Journal of Virology","accessDate":null,"startPage":null,"volume":98,"footnoteNumber":4,"year":null}],"subTopics":[{"id":"65c55d4f9e947c438698b694","name":"Decoding infectivity","path":"/sub-topics/decoding-infectivity","outlineNumber":"3.6.1","slug":"decoding-infectivity","__typename":"Platform_SubTopic","color":{"id":"65c55cbc9e947c438698a319","name":"Green","value":"#68AE9B"},"topic":{"id":"65c55d599e947c438698b7b8","slug":"pathogen-biology","path":"/topics/pathogen-biology"},"intro":{"text":"Rapid progress in molecular and cell biology is enabling a deeper understanding of the mechanisms by which pathogens infect human cells. A crucial step forward has been the development of AI tools such as AlphaFold, which predicts the 3D structure of a protein based on its amino-acid sequence. Such tools can characterise key molecules (including their mechanisms and functions) faster than traditional experimental methods. Likewise, ongoing improvements in the speed and accuracy of DNA sequencing mean there is now a wealth of data on the genomics of pathogenic and non-pathogenic microorganisms."},"description":{"text":"Other work is improving our understanding of how the immune system interacts with other bodily systems in intricate ways. A seemingly simple infection might set off a complex cascade of changes within the body, for example, especially in conditions like long COVID. Conversely, children’s plasma proteome is remodelled by exposure to malaria, leading to natural immunity, and understanding this could lead to new therapeutics.[5](/citation/2025-03-3-6-5/)\n\nBringing such distinct datasets together is challenging but offers the potential for great insights.[6](/citation/2025-03-3-6-6/) It has become possible to predict COVID-19 mortality solely by analysing gene expression in the patient’s blood, for example.[7](/citation/2025-03-3-6-7/) Similarly, rich biological datasets are helping to explain long-established but mysterious patterns in infection biology. For instance, specific changes in the immune system may account for the much higher death rates from COVID-19 among the elderly.[8](/citation/2025-03-3-6-8/)\n\nAI is mining insights from the vast datasets generated by research. For example, machine-learning models can predict how virus proteins will interact with host proteins,[9](/citation/2025-03-3-6-9/) and forecast long-COVID outcomes based on immunological data.[10](/citation/2025-03-3-6-10/) However, AI expertise and infrastructure is currently centred in the rich West: there is an urgent need to expand AI access in low and middle income countries, where it can enable biomedical progress even in low-resource settings."},"anticipationScores":{"text":"The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate: \n\n1. The *uncertainty* related to future science breakthroughs in the field\n2. The *transformative* *effect* anticipated breakthroughs may have on research and society\n3. The *scope for action* in the present in relation to anticipated breakthroughs. \n\nThis chart represents a summary of their responses to each of these elements, which when combined, provide the *Anticipation Potential* for the topic. See [methodology](/science-anticipation/methodology) for more information."},"anticipationScoresImage":{"id":"68e89d6863d1c853e9788c6f","image":{"id":"image_gesda-platform/image-asset/3-6-1-sub-anti-2026_image__3.6.1_sub_anti_2026_t3rsru","url":"https://res.cloudinary.com/shapeable/image/upload/v1760075099/gesda-platform/image-asset/3-6-1-sub-anti-2026_image__3.6.1_sub_anti_2026_t3rsru.webp","url2x":null,"width":1200,"height":1200}},"horizons":[{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a817","name":"3.7.1 - 25-year horizon","slug":"3-7-1-25-year-horizon","intro":{"text":"AI models eliminate animal research"},"description":{"text":"Researchers achieve the elimination of most animal research on pathogens and of most in vitro experiments. Instead, AI models trained on datasets like the Human Cell Atlas provide insights. Gain-of-function research is also performed computationally in most cases. Targeted treatments arise for conditions like multiple sclerosis that are linked to chronic infections from viruses such as Epstein-Barr."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a816","name":"3.7.1 - 10-year horizon","slug":"3-7-1-10-year-horizon","intro":{"text":"Pathogen genotypes reveal likely consequences of infection"},"description":{"text":"A pathogen’s phenotype, including the type of illness it causes, can be reliably predicted from its genotype. AI is in routine use for development of drugs and vaccines."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a815","name":"3.7.1 - 5-year horizon","slug":"3-7-1-5-year-horizon","intro":{"text":"Infection scenarios can be reliably predicted"},"description":{"text":"Reliable predictions of which pathogens will be able to infect which hosts, based on genotype, become possible. These will be underpinned by systematic mutagenesis experiments. Researchers gain improved understanding of the holistic impacts of infections, such as viruses causing cancer, and chronic conditions like Long COVID."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[{"id":"65c55cf49e947c438698aac7","value":"0.506","numericValue":0.506,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68ede4fbaf9e6d6d63271014","value":"0.600","numericValue":0.6,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"embeds":{"citations":[{"slug":"2025-03-3-6-5","url":"https://doi.org/10.1186/s12879-025-10495-4","name":"Malaria exposure remodels the plasma proteome of Ghanaian children","authors":[{"name":"A. M. Mohammed et al."}],"authorShowsEtAl":null,"edition":null,"publication":"BMC Infectious Diseases","accessDate":null,"startPage":157,"volume":25,"footnoteNumber":5,"year":null},{"slug":"2025-03-3-6-6","url":"https://doi.org/10.1016/j.cels.2025.101295","name":"What is the current bottleneck in mapping molecular interaction networks?","authors":[{"name":"M. A. Skinnider et al."}],"authorShowsEtAl":null,"edition":null,"publication":"Cell Systems","accessDate":null,"startPage":101295,"volume":16,"footnoteNumber":6,"year":null},{"slug":"2025-03-3-6-7","url":"https://doi.org/10.1101/2025.05.18.25327658","name":"Minimalistic transcriptomic signatures permit accurate early prediction of COVID-19 mortality","authors":[{"name":"R. Narendra et al."}],"authorShowsEtAl":null,"edition":null,"publication":"medRxiv ","accessDate":null,"startPage":null,"volume":null,"footnoteNumber":7,"year":null},{"slug":"2025-03-3-6-8","url":"https://doi.org/10.1126/scitranslmed.adj5154","name":"Host-microbe multiomic profiling reveals age-dependent immune dysregulation associated with COVID-19 immunopathology","authors":[{"name":"H. Van Phan et al."}],"authorShowsEtAl":null,"edition":null,"publication":"Science Translational Medicine","accessDate":null,"startPage":null,"volume":16,"footnoteNumber":8,"year":null},{"slug":"2025-03-3-6-9","url":"https://doi.org/10.1093/bioinformatics/btab147","name":"DeepViral: prediction of novel virus–host interactions from protein sequences and infectious disease phenotypes","authors":[{"name":"W. Liu-Wei et al."}],"authorShowsEtAl":null,"edition":null,"publication":"Bioinformatics","accessDate":null,"startPage":2722,"volume":37,"footnoteNumber":9,"year":null},{"slug":"2025-03-3-6-10","url":"https://doi.org/10.1101/2025.02.12.25322164","name":"Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors","authors":[{"name":"N. D. Jayavelu et al."}],"authorShowsEtAl":null,"edition":null,"publication":"medRxiv ","accessDate":null,"startPage":null,"volume":null,"footnoteNumber":10,"year":null}],"imageAssets":[]}},{"id":"65c55d4f9e947c438698b6b8","name":"Zoonotics and evolution across species","path":"/sub-topics/zoonotics-and-evolution-across-species","outlineNumber":"3.6.2","slug":"zoonotics-and-evolution-across-species","__typename":"Platform_SubTopic","color":{"id":"65c55cbc9e947c438698a319","name":"Green","value":"#68AE9B"},"topic":{"id":"65c55d599e947c438698b7b8","slug":"pathogen-biology","path":"/topics/pathogen-biology"},"intro":{"text":"Pathogens that jump from other species into humans are the most significant source of new diseases and outbreaks. Predicting and preventing such “zoonoses” remains a challenge,[11](/citation/2025-03-3-6-11/) requiring the integration of molecular and microbiological data with ecological and sociological information."},"description":{"text":"Basic knowledge of microbial life also needs to be improved. Some potential zoonotic pathogens, like H5N1 bird flu, are fairly well-characterised.[12](/citation/2025-03-3-6-12/) However, the majority of bacteria and viruses have not been studied at all. Researchers are working to change this, for instance by bringing all vertebrate-virus associations together in one database[13](/citation/2025-03-3-6-13/) and by using AI to help document unknown viruses.[14](/citation/2025-03-3-6-14/)\n\nWith this knowledge base in place, it should be possible to predict which pathogens are likely to spill over into the human population and how dangerous they would be if they did.[15](/citation/2025-03-3-6-15/) AIs trained on these datasets could help make such predictions, leading to early-warning systems.[16](/citation/2025-03-3-6-16/)\n\nHowever, truly reliable predictions require a recognition that outbreaks are not caused just by highly pathogenic organisms but by human disturbance of ecosystems that allows novel diseases to come into contact with our populations. The One Health framework offers a set of tools with which to attempt this.[17](/citation/2025-03-3-6-17/) Some models of this type have been developed.[18](/citation/2025-03-3-6-18/) However, the One Health approach remains under-used: for example, there is little coordination between surveillance of animal diseases and of human diseases."},"anticipationScores":{"text":"The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate: \n\n1. The *uncertainty* related to future science breakthroughs in the field\n2. The *transformative* *effect* anticipated breakthroughs may have on research and society\n3. The *scope for action* in the present in relation to anticipated breakthroughs. \n\nThis chart represents a summary of their responses to each of these elements, which when combined, provide the *Anticipation Potential* for the topic. See [methodology](/science-anticipation/methodology) for more information."},"anticipationScoresImage":{"id":"68e8919f63d1c853e9788bac","image":{"id":"image_gesda-platform/image-asset/3-6-2-sub-anti-2026_image__3.6.2_sub_anti_2026_vvndpj","url":"https://res.cloudinary.com/shapeable/image/upload/v1760072081/gesda-platform/image-asset/3-6-2-sub-anti-2026_image__3.6.2_sub_anti_2026_vvndpj.webp","url2x":null,"width":1200,"height":1200}},"horizons":[{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a81a","name":"3.7.2 - 25-year horizon","slug":"3-7-2-25-year-horizon","intro":{"text":"AI provides spillover predictions"},"description":{"text":"AI trained on multimodal data gives reliable predictions of future zoonotic spillovers. The modification of vectors and reservoirs begins to prevent spillovers, while targeted animal-vaccination programmes also eradicate potential spillover events. Rabies is close to eradication."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a819","name":"3.7.2 - 10-year horizon","slug":"3-7-2-10-year-horizon","intro":{"text":"A global surveillance system is put in place"},"description":{"text":"A smart and sustainable global surveillance system, based on multiple sources of data including environmental sampling and air sampling, is established. Researchers gain a system-level understanding of the interactions between pathogens, hosts and ecosystems. This enables improved management of the food sector to prevent zoonotic spillovers."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a818","name":"3.7.2 - 5-year horizon","slug":"3-7-2-5-year-horizon","intro":{"text":"Viral traits can be anticipated"},"description":{"text":"Analysis of sequences achieves better anticipation of viral traits, in particular the identification of animal viruses that could spill over into humans. Researchers gain an improved understanding of the dynamics of zoonotic outbreaks, enabling reliable decisions on when to step in to prevent an outbreak becoming an epidemic."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[{"id":"65c55cf49e947c438698ab10","value":"0.547","numericValue":0.547,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68ede520af9e6d6d63271025","value":"0.590","numericValue":0.59,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"embeds":{"citations":[{"slug":"2025-03-3-6-11","url":"https://doi.org/10.1038/nature22975","name":"Host and viral traits predict zoonotic spillover from mammals","authors":[{"name":"K. 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Carlson et al."}],"authorShowsEtAl":null,"edition":null,"publication":"mBio","accessDate":null,"startPage":null,"volume":13,"footnoteNumber":13,"year":null},{"slug":"2025-03-3-6-14","url":"https://doi.org/10.1016/j.cell.2024.09.027","name":"Using artificial intelligence to document the hidden RNA virosphere","authors":[{"name":"X. Hou et al."}],"authorShowsEtAl":null,"edition":null,"publication":"Cell Systems","accessDate":null,"startPage":6929,"volume":187,"footnoteNumber":14,"year":null},{"slug":"2025-03-3-6-15","url":"https://doi.org/10.1016/j.coviro.2023.101346","name":"Predicting zoonotic potential of viruses: where are we?","authors":[{"name":"N. Mollentze and D. G. Streicker"}],"authorShowsEtAl":null,"edition":null,"publication":"Current Opinion in Virology","accessDate":null,"startPage":101346,"volume":61,"footnoteNumber":15,"year":null},{"slug":"2025-03-3-6-16","url":"https://doi.org/10.1289/ehp15937","name":"An integrated machine learning framework to understand zoonotic spillover emergence across anthropogenically modified landscapes","authors":[{"name":"Y. Zhang et al."}],"authorShowsEtAl":null,"edition":null,"publication":"Environmental Health Perspectives","accessDate":null,"startPage":null,"volume":null,"footnoteNumber":16,"year":null},{"slug":"2025-03-3-6-17","url":"https://doi.org/10.1073/pnas.2202871119","name":"Pandemic origins and a One Health approach to preparedness and prevention: solutions based on SARS-CoV-2 and other RNA viruses","authors":[{"name":"G. T. Keusch et al."}],"authorShowsEtAl":null,"edition":null,"publication":"PNAS","accessDate":null,"startPage":null,"volume":119,"footnoteNumber":17,"year":null},{"slug":"2025-03-3-6-18","url":"https://doi.org/10.3201/eid3104.241193","name":"Predictive model for estimating annual Ebolavirus spillover potential","authors":[{"name":"C. T. Telford et al."}],"authorShowsEtAl":null,"edition":null,"publication":"Emerging Infectious Diseases","accessDate":null,"startPage":689,"volume":31,"footnoteNumber":18,"year":null}],"imageAssets":[]}},{"id":"65c55d4f9e947c438698b6b6","name":"Epidemiology and prediction","path":"/sub-topics/epidemiology-and-prediction","outlineNumber":"3.6.3","slug":"epidemiology-and-prediction","__typename":"Platform_SubTopic","color":{"id":"65c55cbc9e947c438698a319","name":"Green","value":"#68AE9B"},"topic":{"id":"65c55d599e947c438698b7b8","slug":"pathogen-biology","path":"/topics/pathogen-biology"},"intro":{"text":"Predicting outbreaks, and tracking them once they begin, is essential to public health.[19](/citation/2025-03-3-6-19/)"},"description":{"text":"Traditional pathogen surveillance methods such as contact tracing can now be combined with other data streams, from genomics[20](/citation/2025-03-3-6-20/) to social media. For instance, improvements in DNA sequencing mean it is now feasible to rapidly reconstruct how a disease outbreak occurred, including tracing it back to source.[21](/citation/2025-03-3-6-21/) An improved understanding of human factors that affect spread,[22](/citation/2025-03-3-6-22/) such as malnutrition and genetic vulnerabilities, could significantly aid the tracking of epidemics.[23](/citation/2025-03-3-6-23/)\n\nHowever, successfully integrating and using these datasets remains a challenge,[24](/citation/2025-03-3-6-24/) especially in low- and middle-income countries where resources are limited. It is likely that our initial attempts to predict the course of outbreaks will underperform due to siloed systems, data latency, and interoperability barriers. While such systems may nevertheless offer some insight, it is likely to be too coarse or arrive too late to be used as the basis for effective action.[25](/citation/2025-03-3-6-25/)\n\nSeveral major opportunities exist. An improved understanding of the interactions between pathogens, which affect when and where outbreaks arise, could enable better predictions. A biobank of infection samples from patients would be a valuable resource for both experimental and computational research. And there is considerable potential to train AI on outbreak datasets and use it to make predictions,[26](/citation/2025-03-3-6-26/) but so far little has been done — in part due to data-access limitations.\n\nThe most effective data ecosystems are likely to be decentralised and trust-based, rather than centralised and coercive. West Africa is a good prototype: pathogen surveillance systems there blend formal and informal health intelligence. Truly resilient systems will arise from diversity, redundancy and local agency, not from top-down architectures. One possible model is to create regional “epidemic foresight nodes” that can detect patterns and simulate response scenarios."},"anticipationScores":{"text":"The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate: \n\n1. The *uncertainty* related to future science breakthroughs in the field\n2. The *transformative* *effect* anticipated breakthroughs may have on research and society\n3. The *scope for action* in the present in relation to anticipated breakthroughs. \n\nThis chart represents a summary of their responses to each of these elements, which when combined, provide the *Anticipation Potential* for the topic. See [methodology](/science-anticipation/methodology) for more information."},"anticipationScoresImage":{"id":"65c55cee9e947c438698a9f0","image":{"id":"image_gesda-platform/image-asset/ic-3-7-3-vector-control-2024_image__3.7.3_mxcelj","url":"https://res.cloudinary.com/shapeable/image/upload/v1726618367/gesda-platform/image-asset/ic-3-7-3-vector-control-2024_image__3.7.3_mxcelj.webp","url2x":null,"width":1200,"height":1200}},"horizons":[{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a81d","name":"3.7.3 - 25-year horizon","slug":"3-7-3-25-year-horizon","intro":{"text":"A global picture of pathogen threats is established"},"description":{"text":"Research achieves a map of the global infectome, analogous to the first maps of the human genome, revealing the total global picture of pathogens infecting humans and animals. Deep, continuous sensing detects and characterises most infections in near-real time. This is enabled by low-cost molecular diagnostics, plus multiple sensing technologies including wearable, environmental and infrastructural. Surveillance systems that mirror the human immune system are established: they are decentralised and adaptive, with built-in redundancies. The most resilient systems are not centrally controlled, but are nonetheless globally connected."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a81c","name":"3.7.3 - 10-year horizon","slug":"3-7-3-10-year-horizon","intro":{"text":"New data sources come online"},"description":{"text":"A dramatic expansion of health-relevant data, including environmental, behavioural, pathogen and host-resilience data, is collected through an array of methods, including wearables, ambient sensors, satellites and low-cost diagnostics. These novel data sources, integrated into improved models, enable better forecasting of outbreaks of diseases such as cholera. Advances in learning, computation and data generation make decentralised surveillance more effective. AI enables better forecasts of outbreaks and their progress."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a81b","name":"3.7.3 - 5-year horizon","slug":"3-7-3-5-year-horizon","intro":{"text":"AI improves outbreak prediction"},"description":{"text":"Attempts at outbreak prediction begin to use AI to integrate multiple data streams such as genomics, human mobility and climate. Improvements in trust and data integration enable major practical gains in outbreak prediction and handling from targeted, community-driven surveillance."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[{"id":"65c55cf49e947c438698ab0c","value":"0.583","numericValue":0.583,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68ede54caf9e6d6d63271034","value":"0.630","numericValue":0.63,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"embeds":{"citations":[{"slug":"2025-03-3-6-19","url":"https://doi.org/10.1101/2025.04.03.25325193","name":"Diagnostic performance and kinetics of hepatitis E viral RNA and IgM antibody test positivity in a genotype 1 outbreak in South Sudan","authors":[{"name":"A. Koyuncu et al."}],"authorShowsEtAl":null,"edition":null,"publication":"medRxiv ","accessDate":null,"startPage":null,"volume":null,"footnoteNumber":19,"year":null},{"slug":"2025-03-3-6-20","url":"https://doi.org/10.3201/eid3106.240930","name":"Genomic surveillance of climate-amplified cholera outbreak, Malawi, 2022–2023","authors":[{"name":"L. Chabuka et al."}],"authorShowsEtAl":null,"edition":null,"publication":"Emerging Infectious Diseases","accessDate":null,"startPage":1090,"volume":31,"footnoteNumber":20,"year":null},{"slug":"2025-03-3-6-21","url":"https://doi.org/10.1101/2025.03.25.645253","name":"Delphy: scalable, near-real-time Bayesian phylogenetics for outbreaks","authors":[{"name":"P. Varilly et al."}],"authorShowsEtAl":null,"edition":null,"publication":"bioRxiv","accessDate":null,"startPage":null,"volume":null,"footnoteNumber":21,"year":null},{"slug":"2025-03-3-6-22","url":"https://doi.org/10.1038/s41562-025-02151-3","name":"Improving mobility data for infectious disease research","authors":[{"name":"N. Kostandova et al."}],"authorShowsEtAl":null,"edition":null,"publication":"Nature Human Behaviour","accessDate":null,"startPage":1309,"volume":9,"footnoteNumber":22,"year":null},{"slug":"2025-03-3-6-23","url":"https://doi.org/10.1126/science.abb4218","name":"The effect of human mobility and control measures on the COVID-19 epidemic in China","authors":[{"name":"M. U. G. Kraemer et al."}],"authorShowsEtAl":null,"edition":null,"publication":"Science","accessDate":null,"startPage":493,"volume":368,"footnoteNumber":23,"year":null},{"slug":"2025-03-3-6-24","url":"https://doi.org/10.1186/s12919-025-00321-9","name":"Data integration and synthesis for pandemic and epidemic intelligence","authors":[{"name":"B. Tornimbene et al."}],"authorShowsEtAl":null,"edition":null,"publication":"BMC Proceedings","accessDate":null,"startPage":12,"volume":19,"footnoteNumber":24,"year":null},{"slug":"2025-03-3-6-25","url":"https://doi.org/10.1101/2025.03.05.25323408","name":"Transmission lineage dynamics and the detection of viral importation in emerging epidemics","authors":[{"name":"J. L.-H. 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These new interventions will be vital because of a host of threats: antimicrobial resistance (AMR), new zoonotic outbreaks and a growing burden of chronic disease that exacerbates the risks from pathogens."},"description":{"text":"Some new therapeutics have already found widespread use. mRNA vaccines were deployed in vast numbers against covid-19, after decades in development hell. So were monoclonal antibodies. Gene editing for inherited diseases is also increasingly mainstream.\n\nOther promising approaches remain largely untapped. One such example is phage therapy — using viruses called bacteriophages to treat bacterial infections.[27](/citation/2025-03-3-6-27/) Phage therapy could be used to treat antimicrobial-resistant bacteria, saving many lives.[28](/citation/2025-03-3-6-28/) A key challenge is to better characterise the interactions between the viruses and their bacterial hosts,[29](/citation/2025-03-3-6-29/) which will help ensure that the chosen phages actually destroy their target organism.[30](/citation/2025-03-3-6-30/)\n\nA second avenue of attack is to hamper the evolution of antimicrobial resistance, ensuring that antibiotics remain useful for longer. This requires an improved understanding of bacterial evolution.[31](/citation/2025-03-3-6-31/) Key challenges include predicting the behaviour of mobile genetic elements[32](/citation/2025-03-3-6-32/) and improving our understanding of how drug treatments trigger the evolution of resistance.[33](/citation/2025-03-3-6-33/),[34](/citation/2025-03-3-6-34/)\n\nTechniques such as cryogenic electron microscopy and AI are enabling rapid progress in our understanding of molecular interactions such as protein-protein interactions.[35](/citation/2025-03-3-6-35/) This promises a multitude of new therapeutic targets that could be targeted rapidly using the tools of synthetic biology. Existing drugs could also be repurposed.[36](/citation/2025-03-3-6-36/)\n\nFinally, the rapid emergence of new pathogens is spurring attempts to develop broad-spectrum treatments such as a pan-coronavirus vaccine.[37](/citation/2025-03-3-6-37/) Multiple methods are being pursued: for instance, it may be possible to develop a “universal antibody vaccine” based on monoclonal antibodies.[38](/citation/2025-03-3-6-38/)"},"anticipationScores":{"text":"The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate: \n\n1. The *uncertainty* related to future science breakthroughs in the field\n2. The *transformative* *effect* anticipated breakthroughs may have on research and society\n3. The *scope for action* in the present in relation to anticipated breakthroughs. \n\nThis chart represents a summary of their responses to each of these elements, which when combined, provide the *Anticipation Potential* for the topic. See [methodology](/science-anticipation/methodology) for more information."},"anticipationScoresImage":{"id":"65c55cee9e947c438698a9f1","image":{"id":"image_gesda-platform/image-asset/ic-3-7-4-outbreak-prevention-2024_image__3.7.4_qtfvqx","url":"https://res.cloudinary.com/shapeable/image/upload/v1726618483/gesda-platform/image-asset/ic-3-7-4-outbreak-prevention-2024_image__3.7.4_qtfvqx.webp","url2x":null,"width":1200,"height":1200}},"horizons":[{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a820","name":"3.7.4 - 25-year horizon","slug":"3-7-4-25-year-horizon","intro":{"text":"Generalised vaccines bring therapeutic advances"},"description":{"text":"Pan-family vaccines and antivirals are developed for major virus families, and synthetic phage therapies offer highly targeted treatments. New treatments for chronic conditions are developed thanks to an improved understanding of pan-disease mechanisms in the human body."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a81f","name":"3.7.4 - 10-year horizon","slug":"3-7-4-10-year-horizon","intro":{"text":"Novel tools create research opportunities"},"description":{"text":"Research brings improved tools to discriminate between natural pathogens and engineered pathogens. AI models microbial ecosystems such as the gut microbiome, enabling targeted interventions."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"65c55ce69e947c438698a81e","name":"3.7.4 - 5-year horizon","slug":"3-7-4-5-year-horizon","intro":{"text":"Breakthrough therapies come online"},"description":{"text":"Precision phage therapy is used to treat antimicrobial-resistant infections, while microbiome interventions treat neurological and psychiatric conditions."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a319","name":"Green","slug":"green","value":"#68AE9B"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[{"id":"65c55cf49e947c438698aac5","value":"0.650","numericValue":0.65,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68ede57caf9e6d6d63271043","value":"0.660","numericValue":0.66,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"embeds":{"citations":[{"slug":"2025-03-3-6-27","url":"https://doi.org/10.1038/s43586-024-00377-5","name":"Phage therapy","authors":[{"name":"M. Skurnik et al."}],"authorShowsEtAl":null,"edition":null,"publication":"Nature Reviews Methods Primers","accessDate":null,"startPage":9,"volume":5,"footnoteNumber":27,"year":null},{"slug":"2025-03-3-6-28","url":"https://doi.org/10.1016/j.mib.2025.102613","name":"A solution to the postantibiotic era: phages as precision medicine","authors":[{"name":"L. J. Getz et al."}],"authorShowsEtAl":null,"edition":null,"publication":"Current Opinion in Microbiology","accessDate":null,"startPage":102613,"volume":86,"footnoteNumber":28,"year":null},{"slug":"2025-03-3-6-29","url":"https://doi.org/10.1146/annurev-virology-100422-125123","name":"Diverse antiphage defenses are widespread among prophages and mobile genetic elements","authors":[{"name":"L. J. Getz and K. L. 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